A Centralized Multi-Agent DRL-Based Trajectory Control Strategy for Unmanned Aerial Vehicle-Enabled Wireless Communications

IF 5.3 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Open Journal of Vehicular Technology Pub Date : 2024-08-28 DOI:10.1109/OJVT.2024.3451143
Getaneh Berie Tarekegn;Rong-Terng Juang;Belayneh Abebe Tesfaw;Hsin-Piao Lin;Huan-Chia Hsu;Robel Berie Tarekegn;Li-Chia Tai
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Abstract

Unmanned aerial vehicles (UAVs) are becoming increasingly popular as mobile base stations due to their flexible deployment and low-cost features, particularly for emergency communications, traffic offloading, and terrestrial communications infrastructure failures. This paper presents an autonomous trajectory control method for multiple UAVs equipped with base stations for UAV-enabled wireless communications. The objective of this work is to address the optimization challenge of maximizing both communication coverage and network throughput for ground users. The proposed multi-aerial base station trajectory control (MATC) scheme employs a two-stage learning approach. Initially, we developed a long short-term memory-based link quality estimation model to assess each user's link quality over time. The trajectory of the aerial base stations is then continuously adjusted through a centralized multi-agent deep reinforcement learning algorithm to optimize communication performance. We evaluated our proposed system using real channel measurement data, i.e., amplitude and phase signal information. Notably, the proposed approach operates solely on received signals from users, without requiring knowledge of their specific locations. The proposed MATC strategy achieves 97.41% communication coverage while maintaining satisfactory system throughput performance. Numerical results demonstrate that the proposed method significantly enhances both communication coverage and network throughput in comparison to the base line algorithms.
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基于 DRL 的无人机无线通信集中式多代理轨迹控制策略
无人飞行器(UAV)因其部署灵活、成本低廉等特点,正日益成为移动基站的热门选择,特别是在应急通信、流量卸载和地面通信基础设施故障等方面。本文介绍了一种针对配备基站的多架无人机的自主轨迹控制方法,用于支持无人机的无线通信。这项工作的目标是解决通信覆盖范围和地面用户网络吞吐量最大化的优化难题。所提出的多航空基站轨迹控制(MATC)方案采用了两阶段学习法。首先,我们开发了一个基于长短期记忆的链路质量估计模型,以评估每个用户随时间变化的链路质量。然后,通过集中式多代理深度强化学习算法不断调整空中基站的轨迹,以优化通信性能。我们利用真实的信道测量数据,即振幅和相位信号信息,对我们提出的系统进行了评估。值得注意的是,所提出的方法只需接收来自用户的信号即可运行,无需了解用户的具体位置。拟议的 MATC 策略实现了 97.41% 的通信覆盖率,同时保持了令人满意的系统吞吐量性能。数值结果表明,与基础算法相比,所提出的方法显著提高了通信覆盖率和网络吞吐量。
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CiteScore
9.60
自引率
0.00%
发文量
25
审稿时长
10 weeks
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